1. Field of the Invention
The present invention relates generally to the field of image processing, and more particularly, to the stochastic adjustment of fundus images having different brightness, contrast and color.
2. Description of Background Art
It is often necessary to view images of an object such as an eye fundus in stereo in order to analyze the three-dimensional structure of the object, the distance from the image capture device to the object, or to measure spatial changes over time. Usually, stereo photographs of the human eye fundus are taken with one camera shifted by a small distance, illuminating the fundus through the pupil as illustrated in FIG. 1. The shape of the eye fundus is generally spherical, so the difference in color and brightness between stereo images depends on the position of the camera, which illuminates the fundus through the pupil at different angles. For example, FIGS. 2a and 2b show a left and right image of an ocular nerve. In the figure, the left part of the left image in FIG. 2a is darker than the left part of the right image in FIG. 2b, and the right part of the right image is darker than the right part of the left image. In order to be able to perform a matching analysis on the images and create a topographical representation of the fundus, these illumination errors must be substantially reduced or eliminated. In addition, it is often desirable to compare two images of the same fundus taken at different times, or with different cameras. This additionally presents a situation where the illumination in each image may be different, requiring correction before appropriate analysis can take place.
It is possible to adjust the brightness, contrast and color of two images using a histogram adjustment method, as proposed by Kanagasingam Yogesan, Robert H. Eikelboom and Chris J. Barry in their paper, xe2x80x9cColour Matching of Serial Retinal Images,xe2x80x9d Lions Eye Institute and Centre for Ophthalmology and Visual Science, Perth, Western Australia, Australia, which is incorporated by reference herein in its entirety. In their paper, the authors propose a color-matching algorithm that equalizes the mean and standard deviation of each of the three colors in the image. First, the entire image is split into the colors red, green and blue; the mean and standard deviation are calculated, and then the histograms of both images are adjusted to equalize the images. The color image is reconstituted by recombining the three channels. The problem with this method of adjustment is that the equalization adjustment is made for the whole image, so the differences in illumination within the images remain unchanged. For example, consider the points 202a and 202b in FIGS. 2a and 2b, respectively. From the figures, it can be seen that point 202a is much darker than point 202b. However, since 202a and 202b actually are the same point on the eye fundus, both points should ideally be illuminated equivalently. Because the Kanagasingram et al. method uses a histogram to adjust the brightness of the whole image, if FIG. 2a were lightened, for example, points 202a and 202b might end up being equally bright, but point 204a, which was originally lighter than point 204b, would now be even brighter, causing increased differences in illumination between the points 204a and 204b. Thus, adjusting the entire image to compensate for different illumination is not a satisfactory solution.
Therefore, what is needed is a way of adjusting differently illuminated images of an eye fundus to compensate for different lighting conditions.
In accordance with the present invention, there is provided a system and method for adjusting differently illuminated images of an eye fundus (106) to reduce and eliminate illumination errors. In one embodiment, two or more images (206, 208) are obtained by an image receiving device (502) that is coupled to a processing computer (500). In another embodiment, the images exist on film or paper, and are converted into computer-readable form by a scanning device. Pixels within each image are assigned to groups (306, 308) of a selected width. Each group forms a line through the image. The lines may be either straight or curved, although a selection of longitudinally-curved lines allows for greater reduction in illumination errors. Each group (306) in the first image (302) is associated with a corresponding group (308) in the other images. Next, the intensity level for at least one color channel is determined for each pixel in each group (306, 308). From this data, the mean intensity level for each group (306,308) is then determined. In one embodiment, the variance of each group (306, 308) is additionally determined. The mean intensity levels for each group (306, 308) are compared in each image (302, 304), and the intensity level of pixels in one or more images are then adjusted so that the nth group in each image will have approximately equal mean intensity levels.